
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report 
import os


print (os.getcwd())

C:\Users\mannr


os.chdir('C:\\Users\\mannr\\OneDrive\\Documents\\acme data science')
print (os.getcwd())

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[1], line 1
----> 1 os.chdir('C:\\Users\\mannr\\OneDrive\\Documents\\acme data science')
      2 print (os.getcwd())

NameError: name 'os' is not defined


df=pd.read_csv('parkinsons.data')
display (df)


import pandas_profiling as pf
display(pf.ProfileReport(df))

C:\Users\mannr\AppData\Local\Temp\ipykernel_22856\2072535499.py:1: DeprecationWarning: `import pandas_profiling` is going to be deprecated by April 1st. Please use `import ydata_profiling` instead.
  import pandas_profiling as pf

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]

Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]


 print(df.info())

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 195 entries, 0 to 194
Data columns (total 24 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   name              195 non-null    object 
 1   MDVP:Fo(Hz)       195 non-null    float64
 2   MDVP:Fhi(Hz)      195 non-null    float64
 3   MDVP:Flo(Hz)      195 non-null    float64
 4   MDVP:Jitter(%)    195 non-null    float64
 5   MDVP:Jitter(Abs)  195 non-null    float64
 6   MDVP:RAP          195 non-null    float64
 7   MDVP:PPQ          195 non-null    float64
 8   Jitter:DDP        195 non-null    float64
 9   MDVP:Shimmer      195 non-null    float64
 10  MDVP:Shimmer(dB)  195 non-null    float64
 11  Shimmer:APQ3      195 non-null    float64
 12  Shimmer:APQ5      195 non-null    float64
 13  MDVP:APQ          195 non-null    float64
 14  Shimmer:DDA       195 non-null    float64
 15  NHR               195 non-null    float64
 16  HNR               195 non-null    float64
 17  status            195 non-null    int64  
 18  RPDE              195 non-null    float64
 19  DFA               195 non-null    float64
 20  spread1           195 non-null    float64
 21  spread2           195 non-null    float64
 22  D2                195 non-null    float64
 23  PPE               195 non-null    float64
dtypes: float64(22), int64(1), object(1)
memory usage: 36.7+ KB
None


display(df.describe())


display(df.isna())


display(df.isna().sum())

name                0
MDVP:Fo(Hz)         0
MDVP:Fhi(Hz)        0
MDVP:Flo(Hz)        0
MDVP:Jitter(%)      0
MDVP:Jitter(Abs)    0
MDVP:RAP            0
MDVP:PPQ            0
Jitter:DDP          0
MDVP:Shimmer        0
MDVP:Shimmer(dB)    0
Shimmer:APQ3        0
Shimmer:APQ5        0
MDVP:APQ            0
Shimmer:DDA         0
NHR                 0
HNR                 0
status              0
RPDE                0
DFA                 0
spread1             0
spread2             0
D2                  0
PPE                 0
dtype: int64


import matplotlib
print(matplotlib.get_backend())  # To check the current backend
matplotlib.use('module://matplotlib_inline.backend_inline')  # To set the backend explicitly

module://matplotlib_inline.backend_inline


plt.figure(figsize=(10, 6))
df.status.hist()
plt.xlabel('Status')
plt.ylabel('Frequencies')
plt.plot()
plt.show()


corr = df.corr()
display(corr)

C:\Users\mannr\AppData\Local\Temp\ipykernel_22856\543053642.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  corr = df.corr()


plt.figure(figsize=(10, 6))
sns.barplot(x="status",y="HNR",data=df);
plt.show()


display(df)


df.drop(['name'],axis=1,inplace=True)
display (df)


x = df.drop(labels=['status'], axis =1)
print(x)
display (x.head())

     MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \
0        119.992       157.302        74.997         0.00784   
1        122.400       148.650       113.819         0.00968   
2        116.682       131.111       111.555         0.01050   
3        116.676       137.871       111.366         0.00997   
4        116.014       141.781       110.655         0.01284   
..           ...           ...           ...             ...   
190      174.188       230.978        94.261         0.00459   
191      209.516       253.017        89.488         0.00564   
192      174.688       240.005        74.287         0.01360   
193      198.764       396.961        74.904         0.00740   
194      214.289       260.277        77.973         0.00567   

     MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \
0             0.00007   0.00370   0.00554     0.01109       0.04374   
1             0.00008   0.00465   0.00696     0.01394       0.06134   
2             0.00009   0.00544   0.00781     0.01633       0.05233   
3             0.00009   0.00502   0.00698     0.01505       0.05492   
4             0.00011   0.00655   0.00908     0.01966       0.06425   
..                ...       ...       ...         ...           ...   
190           0.00003   0.00263   0.00259     0.00790       0.04087   
191           0.00003   0.00331   0.00292     0.00994       0.02751   
192           0.00008   0.00624   0.00564     0.01873       0.02308   
193           0.00004   0.00370   0.00390     0.01109       0.02296   
194           0.00003   0.00295   0.00317     0.00885       0.01884   

     MDVP:Shimmer(dB)  ...  MDVP:APQ  Shimmer:DDA      NHR     HNR      RPDE  \
0               0.426  ...   0.02971      0.06545  0.02211  21.033  0.414783   
1               0.626  ...   0.04368      0.09403  0.01929  19.085  0.458359   
2               0.482  ...   0.03590      0.08270  0.01309  20.651  0.429895   
3               0.517  ...   0.03772      0.08771  0.01353  20.644  0.434969   
4               0.584  ...   0.04465      0.10470  0.01767  19.649  0.417356   
..                ...  ...       ...          ...      ...     ...       ...   
190             0.405  ...   0.02745      0.07008  0.02764  19.517  0.448439   
191             0.263  ...   0.01879      0.04812  0.01810  19.147  0.431674   
192             0.256  ...   0.01667      0.03804  0.10715  17.883  0.407567   
193             0.241  ...   0.01588      0.03794  0.07223  19.020  0.451221   
194             0.190  ...   0.01373      0.03078  0.04398  21.209  0.462803   

          DFA   spread1   spread2        D2       PPE  
0    0.815285 -4.813031  0.266482  2.301442  0.284654  
1    0.819521 -4.075192  0.335590  2.486855  0.368674  
2    0.825288 -4.443179  0.311173  2.342259  0.332634  
3    0.819235 -4.117501  0.334147  2.405554  0.368975  
4    0.823484 -3.747787  0.234513  2.332180  0.410335  
..        ...       ...       ...       ...       ...  
190  0.657899 -6.538586  0.121952  2.657476  0.133050  
191  0.683244 -6.195325  0.129303  2.784312  0.168895  
192  0.655683 -6.787197  0.158453  2.679772  0.131728  
193  0.643956 -6.744577  0.207454  2.138608  0.123306  
194  0.664357 -5.724056  0.190667  2.555477  0.148569  

[195 rows x 22 columns]


y = df['status']
display(y.head())

0    1
1    1
2    1
3    1
4    1
Name: status, dtype: int64


x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=40)
print (x.shape,y.shape)
print(x_train.shape,x_test.shape,y_train.shape,y_test.shape)

(195, 22) (195,)
(156, 22) (39, 22) (156,) (39,)


print(x_train)

     MDVP:Fo(Hz)  MDVP:Fhi(Hz)  MDVP:Flo(Hz)  MDVP:Jitter(%)  \
147      183.520       216.814       161.340         0.01466   
86       178.222       202.450       141.047         0.00321   
179      148.143       155.982       135.041         0.00392   
69       148.090       162.824        67.343         0.00762   
125      145.174       198.109        80.637         0.00733   
..           ...           ...           ...             ...   
50       124.445       135.069       117.495         0.00431   
184      116.848       217.552        99.503         0.00531   
165      236.200       244.663       102.137         0.00277   
7        107.332       113.840       104.315         0.00290   
70       142.729       162.408        65.476         0.00831   

     MDVP:Jitter(Abs)  MDVP:RAP  MDVP:PPQ  Jitter:DDP  MDVP:Shimmer  \
147           0.00008   0.00849   0.00819     0.02546       0.06050   
86            0.00002   0.00163   0.00194     0.00488       0.03759   
179           0.00003   0.00204   0.00231     0.00612       0.01450   
69            0.00005   0.00467   0.00354     0.01400       0.05428   
125           0.00005   0.00414   0.00422     0.01242       0.02362   
..                ...       ...       ...         ...           ...   
50            0.00003   0.00141   0.00167     0.00422       0.02184   
184           0.00005   0.00260   0.00346     0.00780       0.01795   
165           0.00001   0.00154   0.00153     0.00462       0.02448   
7             0.00003   0.00144   0.00182     0.00431       0.01567   
70            0.00006   0.00469   0.00419     0.01407       0.03485   

     MDVP:Shimmer(dB)  ...  MDVP:APQ  Shimmer:DDA      NHR     HNR      RPDE  \
147             0.618  ...   0.06359      0.08595  0.06057  14.367  0.478024   
86              0.327  ...   0.02784      0.06219  0.03151  15.924  0.598714   
179             0.131  ...   0.01263      0.02175  0.00540  23.683  0.398499   
69              0.497  ...   0.03635      0.10070  0.02431  21.718  0.487407   
125             0.233  ...   0.01944      0.03706  0.01874  18.857  0.637518   
..                ...  ...       ...          ...      ...     ...       ...   
50              0.197  ...   0.01685      0.03724  0.00479  25.135  0.553134   
184             0.163  ...   0.01756      0.02429  0.01179  22.085  0.663842   
165             0.217  ...   0.01621      0.04231  0.00620  24.078  0.469928   
7               0.134  ...   0.01256      0.02487  0.00344  26.892  0.637420   
70              0.365  ...   0.02949      0.05605  0.02599  20.264  0.489345   

          DFA   spread1   spread2        D2       PPE  
147  0.768974 -4.276605  0.355736  3.142364  0.336085  
86   0.712199 -6.366916  0.335753  2.654271  0.144614  
179  0.778349 -5.711205  0.240875  2.845109  0.192730  
69   0.727313 -6.261141  0.120956  2.137075  0.141958  
125  0.735546 -5.594275  0.127950  1.765957  0.222716  
..        ...       ...       ...       ...       ...  
50   0.775933 -6.650471  0.254498  1.840198  0.103561  
184  0.656516 -5.198864  0.206768  2.120412  0.252404  
165  0.628232 -6.816086  0.172270  2.235197  0.119652  
7    0.763262 -6.167603  0.183721  2.064693  0.163755  
70   0.730387 -5.720868  0.158830  2.277927  0.180828  

[156 rows x 22 columns]


print(y_train)

147    1
86     1
179    1
69     1
125    1
      ..
50     0
184    0
165    0
7      1
70     1
Name: status, Length: 156, dtype: int64


log_reg = LogisticRegression().fit(x_train, y_train)

train_preds = log_reg.predict(x_train)

print("Model accuracy on train is: ", accuracy_score(y_train, train_preds))


test_preds = log_reg.predict(x_test)

print("Model accuracy on test is: ", accuracy_score(y_test, test_preds))
print('*'*50)

print("confusion_matrix train is:\n ", confusion_matrix(y_train, train_preds))
print("confusion_matrix test is:\n ", confusion_matrix(y_test, test_preds))
print('\nClassification Report Train is ')
print(classification_report (y_train, train_preds))
print('\nClassification Report Test is ')
print(classification_report (y_test, test_preds))

Model accuracy on train is:  0.8782051282051282
Model accuracy on test is:  0.8461538461538461
**************************************************
confusion_matrix train is:
  [[ 25  15]
 [  4 112]]
confusion_matrix test is:
  [[ 5  3]
 [ 3 28]]

Classification Report Train is 
              precision    recall  f1-score   support

           0       0.86      0.62      0.72        40
           1       0.88      0.97      0.92       116

    accuracy                           0.88       156
   macro avg       0.87      0.80      0.82       156
weighted avg       0.88      0.88      0.87       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.62      0.62      0.62         8
           1       0.90      0.90      0.90        31

    accuracy                           0.85        39
   macro avg       0.76      0.76      0.76        39
weighted avg       0.85      0.85      0.85        39

C:\Users\mannr\anaconda3\lib\site-packages\sklearn\linear_model\_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(


full_pred = log_reg.predict(x)
print(accuracy_score(y,full_pred))

0.8717948717948718


RF=RandomForestClassifier().fit(x_train,y_train)
 
train_preds2 = RF.predict(x_train)

print("Model accuracy on train is: ", accuracy_score(y_train, train_preds2))


test_preds2 = RF.predict(x_test)

print("Model accuracy on test is: ", accuracy_score(y_test, test_preds2))


print("confusion_matrix train is:\n ", confusion_matrix(y_train, train_preds2))
print("confusion_matrix test is:\n ", confusion_matrix(y_test, test_preds2))
print('\nClassification Report Train is ')
print(classification_report (y_train, train_preds2))
print('\nClassification Report Test is ')
print(classification_report (y_test, test_preds2))

Model accuracy on train is:  1.0
Model accuracy on test is:  0.8717948717948718
confusion_matrix train is:
  [[ 40   0]
 [  0 116]]
confusion_matrix test is:
  [[ 5  3]
 [ 2 29]]

Classification Report Train is 
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        40
           1       1.00      1.00      1.00       116

    accuracy                           1.00       156
   macro avg       1.00      1.00      1.00       156
weighted avg       1.00      1.00      1.00       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.71      0.62      0.67         8
           1       0.91      0.94      0.92        31

    accuracy                           0.87        39
   macro avg       0.81      0.78      0.79        39
weighted avg       0.87      0.87      0.87        39


final2 = RF.predict(x)
print(accuracy_score(y, final2))

0.9743589743589743


print((y_test !=test_preds2).sum(),'/',((y_test == test_preds2).sum()+(y_test != test_preds2).sum()))

5 / 39


from sklearn.naive_bayes import GaussianNB

NB=GaussianNB()
NB.fit(x_train,y_train)

train_preds4 = NB.predict(x_train)

print("Model accuracy on train is: ", accuracy_score(y_train, train_preds4))

test_preds4 = NB.predict(x_test)

print("Model accuracy on test is: ", accuracy_score(y_test, test_preds4))
print('*'*50)

print("confusion_matrix train is: \n", confusion_matrix(y_train, train_preds4))
print("confusion_matrix test is:\n ", confusion_matrix(y_test, test_preds4))
print('Wrong predictions out of total')
print('*'*50)
print('\nClassification Report Train is ')
print(classification_report (y_train, train_preds4))
print('\nClassification Report Test is ')
print(classification_report (y_test, test_preds4))

Model accuracy on train is:  0.7307692307692307
Model accuracy on test is:  0.6923076923076923
**************************************************
confusion_matrix train is: 
 [[38  2]
 [40 76]]
confusion_matrix test is:
  [[ 8  0]
 [12 19]]
Wrong predictions out of total
**************************************************

Classification Report Train is 
              precision    recall  f1-score   support

           0       0.49      0.95      0.64        40
           1       0.97      0.66      0.78       116

    accuracy                           0.73       156
   macro avg       0.73      0.80      0.71       156
weighted avg       0.85      0.73      0.75       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.40      1.00      0.57         8
           1       1.00      0.61      0.76        31

    accuracy                           0.69        39
   macro avg       0.70      0.81      0.67        39
weighted avg       0.88      0.69      0.72        39


from sklearn.ensemble import RandomForestClassifier
RF = RandomForestClassifier(n_estimators=100, criterion='entropy')
RF.fit(x_train,y_train)
RF_pred = RF.predict(x)
print (RF_pred)

print('\n Accuracy Score')
print (accuracy_score (y,RF_pred))
print('\nClassification Report')
print(classification_report (y,RF_pred))
print('Confusion Matrix')
print (confusion_matrix (y,RF_pred))

[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1
 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0
 1 0 1 0 0 0 0 0 0 0]

 Accuracy Score
0.9794871794871794

Classification Report
              precision    recall  f1-score   support

           0       0.96      0.96      0.96        48
           1       0.99      0.99      0.99       147

    accuracy                           0.98       195
   macro avg       0.97      0.97      0.97       195
weighted avg       0.98      0.98      0.98       195

Confusion Matrix
[[ 46   2]
 [  2 145]]


from sklearn.neighbors import KNeighborsClassifier

KNN = KNeighborsClassifier().fit(x_train,y_train)
 
train_preds5 = KNN.predict(x_train)

print("Model accuracy on train is: ", accuracy_score(y_train, train_preds5))


test_preds5 = KNN.predict(x_test)

print("Model accuracy on test is: ", accuracy_score(y_test, test_preds5))
print('*'*50)

print("confusion_matrix train is:\n ", confusion_matrix(y_train, train_preds5))
print("confusion_matrix test is:\n ", confusion_matrix(y_test, test_preds5))
print('Wrong predictions out of total')
print('*'*50)
print('\nClassification Report Train is ')
print(classification_report (y_train, train_preds5))
print('\nClassification Report Test is ')
print(classification_report (y_test, test_preds5))

Model accuracy on train is:  0.9102564102564102
Model accuracy on test is:  0.8461538461538461
**************************************************
confusion_matrix train is:
  [[ 30  10]
 [  4 112]]
confusion_matrix test is:
  [[ 4  4]
 [ 2 29]]
Wrong predictions out of total
**************************************************

Classification Report Train is 
              precision    recall  f1-score   support

           0       0.88      0.75      0.81        40
           1       0.92      0.97      0.94       116

    accuracy                           0.91       156
   macro avg       0.90      0.86      0.88       156
weighted avg       0.91      0.91      0.91       156


Classification Report Test is 
              precision    recall  f1-score   support

           0       0.67      0.50      0.57         8
           1       0.88      0.94      0.91        31

    accuracy                           0.85        39
   macro avg       0.77      0.72      0.74        39
weighted avg       0.84      0.85      0.84        39


final_preds5 = KNN.predict(x)
print("Accuracy of full dataset is : ", accuracy_score(y,final_preds5))

Accuracy of full dataset is :  0.8974358974358975


print((y_test !=test_preds5).sum(),'/',((y_test == test_preds5).sum()+(y_test != test_preds5).sum()))

print('*'*50)

print('KappaScore is: ', metrics.cohen_kappa_score(y_test,test_preds5))

6 / 39
**************************************************
KappaScore is:  0.48


from sklearn.svm import SVC
SVM_SIG = SVC (kernel = 'sigmoid')
SVM_SIG.fit(x_train,y_train)

train_preds6=SVM_SIG.predict(x_train)
print("Accuracy of train data set is : ", accuracy_score(y_train,train_preds6))
print('*'*50)

SVM_SIG_pred = SVM_SIG.predict(x)
print (SVM_SIG_pred)
print('*'*50)

print('\n Accuracy Score')
print (accuracy_score (y,SVM_SIG_pred))
print('\nClassification Report')
print(classification_report (y,SVM_SIG_pred))
print('*'*50)

print('Confusion Matrix')
print (confusion_matrix (y,SVM_SIG_pred))

Accuracy of train data set is :  0.7435897435897436
**************************************************
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1]
**************************************************

 Accuracy Score
0.7538461538461538

Classification Report
              precision    recall  f1-score   support

           0       0.00      0.00      0.00        48
           1       0.75      1.00      0.86       147

    accuracy                           0.75       195
   macro avg       0.38      0.50      0.43       195
weighted avg       0.57      0.75      0.65       195

**************************************************
Confusion Matrix
[[  0  48]
 [  0 147]]

C:\Users\mannr\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\mannr\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\mannr\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))


from sklearn.svm import SVC
SVM_SIG = SVC (kernel = 'poly')
SVM_SIG.fit(x_train,y_train)

train_preds6=SVM_SIG.predict(x_train)
print("Accuracy of train data set is : ", accuracy_score(y_train,train_preds6))
print('*'*50)

SVM_SIG_pred = SVM_SIG.predict(x)
print (SVM_SIG_pred)
print('*'*50)

print('\n Accuracy Score')
print (accuracy_score (y,SVM_SIG_pred))
print('\nClassification Report')
print(classification_report (y,SVM_SIG_pred))
print('*'*50)

print('Confusion Matrix')
print (confusion_matrix (y,SVM_SIG_pred))

Accuracy of train data set is :  0.8205128205128205
**************************************************
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 1
 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1]
**************************************************

 Accuracy Score
0.8307692307692308

Classification Report
              precision    recall  f1-score   support

           0       0.89      0.35      0.51        48
           1       0.82      0.99      0.90       147

    accuracy                           0.83       195
   macro avg       0.86      0.67      0.70       195
weighted avg       0.84      0.83      0.80       195

**************************************************
Confusion Matrix
[[ 17  31]
 [  2 145]]


from sklearn.svm import SVC
SVM_SIG = SVC (kernel = 'sigmoid')
SVM_SIG.fit(x_train,y_train)

train_preds6=SVM_SIG.predict(x_train)
print("Accuracy of train data set is : ", accuracy_score(y_train,train_preds6))
print('*'*50)

SVM_SIG_pred = SVM_SIG.predict(x)
print (SVM_SIG_pred)
print('*'*50)

print('\n Accuracy Score')
print (accuracy_score (y,SVM_SIG_pred))
print('\nClassification Report')
print(classification_report (y,SVM_SIG_pred))
print('*'*50)

print('Confusion Matrix')
print (confusion_matrix (y,SVM_SIG_pred))

Accuracy of train data set is :  0.7435897435897436
**************************************************
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1]
**************************************************

 Accuracy Score
0.7538461538461538

Classification Report
              precision    recall  f1-score   support

           0       0.00      0.00      0.00        48
           1       0.75      1.00      0.86       147

    accuracy                           0.75       195
   macro avg       0.38      0.50      0.43       195
weighted avg       0.57      0.75      0.65       195

**************************************************
Confusion Matrix
[[  0  48]
 [  0 147]]

C:\Users\mannr\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\mannr\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))
C:\Users\mannr\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
  _warn_prf(average, modifier, msg_start, len(result))


from sklearn.svm import SVC
SVM_SIG = SVC (kernel = 'rbf')
SVM_SIG.fit(x_train,y_train)

train_preds6=SVM_SIG.predict(x_train)
print("Accuracy of train data set is : ", accuracy_score(y_train,train_preds6))
print('*'*50)

SVM_SIG_pred = SVM_SIG.predict(x)
print (SVM_SIG_pred)
print('*'*50)

print('\n Accuracy Score')
print (accuracy_score (y,SVM_SIG_pred))
print('\nClassification Report')
print(classification_report (y,SVM_SIG_pred))
print('*'*50)

print('Confusion Matrix')
print (confusion_matrix (y,SVM_SIG_pred))

Accuracy of train data set is :  0.8141025641025641
**************************************************
[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1]
**************************************************

 Accuracy Score
0.8153846153846154

Classification Report
              precision    recall  f1-score   support

           0       0.93      0.27      0.42        48
           1       0.81      0.99      0.89       147

    accuracy                           0.82       195
   macro avg       0.87      0.63      0.65       195
weighted avg       0.84      0.82      0.77       195

**************************************************
Confusion Matrix
[[ 13  35]
 [  1 146]]

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
count	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	...	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000	195.000000
mean	154.228641	197.104918	116.324631	0.006220	0.000044	0.003306	0.003446	0.009920	0.029709	0.282251	...	0.046993	0.024847	21.885974	0.753846	0.498536	0.718099	-5.684397	0.226510	2.381826	0.206552
std	41.390065	91.491548	43.521413	0.004848	0.000035	0.002968	0.002759	0.008903	0.018857	0.194877	...	0.030459	0.040418	4.425764	0.431878	0.103942	0.055336	1.090208	0.083406	0.382799	0.090119
min	88.333000	102.145000	65.476000	0.001680	0.000007	0.000680	0.000920	0.002040	0.009540	0.085000	...	0.013640	0.000650	8.441000	0.000000	0.256570	0.574282	-7.964984	0.006274	1.423287	0.044539
25%	117.572000	134.862500	84.291000	0.003460	0.000020	0.001660	0.001860	0.004985	0.016505	0.148500	...	0.024735	0.005925	19.198000	1.000000	0.421306	0.674758	-6.450096	0.174351	2.099125	0.137451
50%	148.790000	175.829000	104.315000	0.004940	0.000030	0.002500	0.002690	0.007490	0.022970	0.221000	...	0.038360	0.011660	22.085000	1.000000	0.495954	0.722254	-5.720868	0.218885	2.361532	0.194052
75%	182.769000	224.205500	140.018500	0.007365	0.000060	0.003835	0.003955	0.011505	0.037885	0.350000	...	0.060795	0.025640	25.075500	1.000000	0.587562	0.761881	-5.046192	0.279234	2.636456	0.252980
max	260.105000	592.030000	239.170000	0.033160	0.000260	0.021440	0.019580	0.064330	0.119080	1.302000	...	0.169420	0.314820	33.047000	1.000000	0.685151	0.825288	-2.434031	0.450493	3.671155	0.527367

	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	MDVP:Shimmer(dB)	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
MDVP:Fo(Hz)	1.000000	0.400985	0.596546	-0.118003	-0.382027	-0.076194	-0.112165	-0.076213	-0.098374	-0.073742	...	-0.094732	-0.021981	0.059144	-0.383535	-0.383894	-0.446013	-0.413738	-0.249450	0.177980	-0.372356
MDVP:Fhi(Hz)	0.400985	1.000000	0.084951	0.102086	-0.029198	0.097177	0.091126	0.097150	0.002281	0.043465	...	-0.003733	0.163766	-0.024893	-0.166136	-0.112404	-0.343097	-0.076658	-0.002954	0.176323	-0.069543
MDVP:Flo(Hz)	0.596546	0.084951	1.000000	-0.139919	-0.277815	-0.100519	-0.095828	-0.100488	-0.144543	-0.119089	...	-0.150737	-0.108670	0.210851	-0.380200	-0.400143	-0.050406	-0.394857	-0.243829	-0.100629	-0.340071
MDVP:Jitter(%)	-0.118003	0.102086	-0.139919	1.000000	0.935714	0.990276	0.974256	0.990276	0.769063	0.804289	...	0.746635	0.906959	-0.728165	0.278220	0.360673	0.098572	0.693577	0.385123	0.433434	0.721543
MDVP:Jitter(Abs)	-0.382027	-0.029198	-0.277815	0.935714	1.000000	0.922911	0.897778	0.922913	0.703322	0.716601	...	0.697170	0.834972	-0.656810	0.338653	0.441839	0.175036	0.735779	0.388543	0.310694	0.748162
MDVP:RAP	-0.076194	0.097177	-0.100519	0.990276	0.922911	1.000000	0.957317	1.000000	0.759581	0.790652	...	0.744919	0.919521	-0.721543	0.266668	0.342140	0.064083	0.648328	0.324407	0.426605	0.670999
MDVP:PPQ	-0.112165	0.091126	-0.095828	0.974256	0.897778	0.957317	1.000000	0.957319	0.797826	0.839239	...	0.763592	0.844604	-0.731510	0.288698	0.333274	0.196301	0.716489	0.407605	0.412524	0.769647
Jitter:DDP	-0.076213	0.097150	-0.100488	0.990276	0.922913	1.000000	0.957319	1.000000	0.759555	0.790621	...	0.744901	0.919548	-0.721494	0.266646	0.342079	0.064026	0.648328	0.324377	0.426556	0.671005
MDVP:Shimmer	-0.098374	0.002281	-0.144543	0.769063	0.703322	0.759581	0.797826	0.759555	1.000000	0.987258	...	0.987626	0.722194	-0.835271	0.367430	0.447424	0.159954	0.654734	0.452025	0.507088	0.693771
MDVP:Shimmer(dB)	-0.073742	0.043465	-0.119089	0.804289	0.716601	0.790652	0.839239	0.790621	0.987258	1.000000	...	0.963202	0.744477	-0.827805	0.350697	0.410684	0.165157	0.652547	0.454314	0.512233	0.695058
Shimmer:APQ3	-0.094717	-0.003743	-0.150747	0.746625	0.697153	0.744912	0.763580	0.744894	0.987625	0.963198	...	1.000000	0.716207	-0.827123	0.347617	0.435242	0.151124	0.610967	0.402243	0.467265	0.645377
Shimmer:APQ5	-0.070682	-0.009997	-0.101095	0.725561	0.648961	0.709927	0.786780	0.709907	0.982835	0.973751	...	0.960072	0.658080	-0.813753	0.351148	0.399903	0.213873	0.646809	0.457195	0.502174	0.702456
MDVP:APQ	-0.077774	0.004937	-0.107293	0.758255	0.648793	0.737455	0.804139	0.737439	0.950083	0.960977	...	0.896647	0.694019	-0.800407	0.364316	0.451379	0.157276	0.673158	0.502188	0.536869	0.721694
Shimmer:DDA	-0.094732	-0.003733	-0.150737	0.746635	0.697170	0.744919	0.763592	0.744901	0.987626	0.963202	...	1.000000	0.716215	-0.827130	0.347608	0.435237	0.151132	0.610971	0.402223	0.467261	0.645389
NHR	-0.021981	0.163766	-0.108670	0.906959	0.834972	0.919521	0.844604	0.919548	0.722194	0.744477	...	0.716215	1.000000	-0.714072	0.189429	0.370890	-0.131882	0.540865	0.318099	0.470949	0.552591
HNR	0.059144	-0.024893	0.210851	-0.728165	-0.656810	-0.721543	-0.731510	-0.721494	-0.835271	-0.827805	...	-0.827130	-0.714072	1.000000	-0.361515	-0.598736	-0.008665	-0.673210	-0.431564	-0.601401	-0.692876
status	-0.383535	-0.166136	-0.380200	0.278220	0.338653	0.266668	0.288698	0.266646	0.367430	0.350697	...	0.347608	0.189429	-0.361515	1.000000	0.308567	0.231739	0.564838	0.454842	0.340232	0.531039
RPDE	-0.383894	-0.112404	-0.400143	0.360673	0.441839	0.342140	0.333274	0.342079	0.447424	0.410684	...	0.435237	0.370890	-0.598736	0.308567	1.000000	-0.110950	0.591117	0.479905	0.236931	0.545886
DFA	-0.446013	-0.343097	-0.050406	0.098572	0.175036	0.064083	0.196301	0.064026	0.159954	0.165157	...	0.151132	-0.131882	-0.008665	0.231739	-0.110950	1.000000	0.195668	0.166548	-0.165381	0.270445
spread1	-0.413738	-0.076658	-0.394857	0.693577	0.735779	0.648328	0.716489	0.648328	0.654734	0.652547	...	0.610971	0.540865	-0.673210	0.564838	0.591117	0.195668	1.000000	0.652358	0.495123	0.962435
spread2	-0.249450	-0.002954	-0.243829	0.385123	0.388543	0.324407	0.407605	0.324377	0.452025	0.454314	...	0.402223	0.318099	-0.431564	0.454842	0.479905	0.166548	0.652358	1.000000	0.523532	0.644711
D2	0.177980	0.176323	-0.100629	0.433434	0.310694	0.426605	0.412524	0.426556	0.507088	0.512233	...	0.467261	0.470949	-0.601401	0.340232	0.236931	-0.165381	0.495123	0.523532	1.000000	0.480585
PPE	-0.372356	-0.069543	-0.340071	0.721543	0.748162	0.670999	0.769647	0.671005	0.693771	0.695058	...	0.645389	0.552591	-0.692876	0.531039	0.545886	0.270445	0.962435	0.644711	0.480585	1.000000

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	phon_R01_S01_1	119.992	157.302	74.997	0.00784	0.00007	0.00370	0.00554	0.01109	0.04374	...	0.06545	0.02211	21.033	1	0.414783	0.815285	-4.813031	0.266482	2.301442	0.284654
1	phon_R01_S01_2	122.400	148.650	113.819	0.00968	0.00008	0.00465	0.00696	0.01394	0.06134	...	0.09403	0.01929	19.085	1	0.458359	0.819521	-4.075192	0.335590	2.486855	0.368674
2	phon_R01_S01_3	116.682	131.111	111.555	0.01050	0.00009	0.00544	0.00781	0.01633	0.05233	...	0.08270	0.01309	20.651	1	0.429895	0.825288	-4.443179	0.311173	2.342259	0.332634
3	phon_R01_S01_4	116.676	137.871	111.366	0.00997	0.00009	0.00502	0.00698	0.01505	0.05492	...	0.08771	0.01353	20.644	1	0.434969	0.819235	-4.117501	0.334147	2.405554	0.368975
4	phon_R01_S01_5	116.014	141.781	110.655	0.01284	0.00011	0.00655	0.00908	0.01966	0.06425	...	0.10470	0.01767	19.649	1	0.417356	0.823484	-3.747787	0.234513	2.332180	0.410335
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	phon_R01_S50_2	174.188	230.978	94.261	0.00459	0.00003	0.00263	0.00259	0.00790	0.04087	...	0.07008	0.02764	19.517	0	0.448439	0.657899	-6.538586	0.121952	2.657476	0.133050
191	phon_R01_S50_3	209.516	253.017	89.488	0.00564	0.00003	0.00331	0.00292	0.00994	0.02751	...	0.04812	0.01810	19.147	0	0.431674	0.683244	-6.195325	0.129303	2.784312	0.168895
192	phon_R01_S50_4	174.688	240.005	74.287	0.01360	0.00008	0.00624	0.00564	0.01873	0.02308	...	0.03804	0.10715	17.883	0	0.407567	0.655683	-6.787197	0.158453	2.679772	0.131728
193	phon_R01_S50_5	198.764	396.961	74.904	0.00740	0.00004	0.00370	0.00390	0.01109	0.02296	...	0.03794	0.07223	19.020	0	0.451221	0.643956	-6.744577	0.207454	2.138608	0.123306
194	phon_R01_S50_6	214.289	260.277	77.973	0.00567	0.00003	0.00295	0.00317	0.00885	0.01884	...	0.03078	0.04398	21.209	0	0.462803	0.664357	-5.724056	0.190667	2.555477	0.148569

	name	MDVP:Fo(Hz)	MDVP:Fhi(Hz)	MDVP:Flo(Hz)	MDVP:Jitter(%)	MDVP:Jitter(Abs)	MDVP:RAP	MDVP:PPQ	Jitter:DDP	MDVP:Shimmer	...	Shimmer:DDA	NHR	HNR	status	RPDE	DFA	spread1	spread2	D2	PPE
0	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
1	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
2	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
3	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
4	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
190	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
191	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
192	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
193	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
194	False	False	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False